Writing

AI

If you can’t be bothered to write it, why should I be bothered to read it?

I keep seeing this line floating around the internet at the moment:

“If you can’t be bothered putting the effort into writing the article, why should I be bothered reading it?”

It’s a sharp, pithy phrase. Exactly the sort of thing that does well on social media. And I understand where the sentiment comes from.

The missing middle of startup funding

There was a time, not that long ago, when starting a small business meant going to see your local bank manager. This sounds almost quaint now. A friendly person in a branch. Someone who may have known you, or at least had access to your history with the bank. You would turn up with a business plan, talk through the numbers, and ask for a loan. Maybe it was unsecured. More likely it was secured against your house, which you probably had mortgaged with the same bank. The bank knew your salary, your spending habits, your debts, your reliability. You were not an abstract risk profile. You were a person in a place, with a track record.

What gets VC funding now?

I was recently asked to join a local conference panel about what investors are looking for in 2026. The session ranged from angel and seed investing to scale-up capital, bank lending and company valuations. But the question I kept coming back to was narrower: what has changed over the last 12 months, and why does the old early-stage fundraising playbook feel less reliable than it used to?

What if Europe is copying the wrong AI strategy?

A lot of the articles I’m reading about AI at the moment seem to make the same assumption: that the future of AI will require vast amounts of centralised compute. Bigger models. Bigger data centres. Bigger chip orders. Bigger energy requirements. Bigger capital commitments.